目标检测模型-YOLOvX-Pytorch版本代码

<div align="center"><img src="assets/logo.png" width="350"></div> <img src="assets/demo.png" > ## Introduction YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. For more details, please refer to our [report on Arxiv](https://arxiv.org/abs/2107.08430). This repo is an implementation of PyTorch version YOLOX, there is also a [MegEngine implementation](https://github.com/MegEngine/YOLOX). <img src="assets/git_fig.png" width="1000" > ## Updates!! * 【2023/02/28】 We support assignment visualization tool, see doc [here](./docs/assignment_visualization.md). * 【2022/04/14】 We support jit compile op. * 【2021/08/19】 We optimize the training process with **2x** faster training and **~1%** higher performance! See [notes](docs/updates_note.md) for more details. * 【2021/08/05】 We release [MegEngine version YOLOX](https://github.com/MegEngine/YOLOX). * 【2021/07/28】 We fix the fatal error of [memory leak](https://github.com/Megvii-BaseDetection/YOLOX/issues/103) * 【2021/07/26】 We now support [MegEngine](https://github.com/Megvii-BaseDetection/YOLOX/tree/main/demo/MegEngine) deployment. * 【2021/07/20】 We have released our technical report on [Arxiv](https://arxiv.org/abs/2107.08430). ## Coming soon - [ ] YOLOX-P6 and larger model. - [ ] Objects365 pretrain. - [ ] Transformer modules. - [ ] More features in need. ## Benchmark #### Standard Models. |Model |size |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 | Speed V100<br>(ms) | Params<br>(M) |FLOPs<br>(G)| weights | | ------ |:---: | :---: | :---: |:---: |:---: | :---: | :----: | |[YOLOX-s](./exps/default/yolox_s.py) |640 |40.5 |40.5 |9.8 |9.0 | 26.8 | [github](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.pth) | |[YOLOX-m](./exps/default/yolox_m.py) |640 |46.9 |47.2 |12.3 |25.3 |73.8| [github](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_m.pth) | |[YOLOX-l](./exps/default/yolox_l.py) |640 |49.7 |50.1 |14.5 |54.2| 155.6 | [github](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_l.pth) | |[YOLOX-x](./exps/default/yolox_x.py) |640 |51.1 |**51.5** | 17.3 |99.1 |281.9 | [github](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_x.pth) | |[YOLOX-Darknet53](./exps/default/yolov3.py) |640 | 47.7 | 48.0 | 11.1 |63.7 | 185.3 | [github](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_darknet.pth) | <details> <summary>Legacy models</summary> |Model |size |mAP<sup>test<br>0.5:0.95 | Speed V100<br>(ms) | Params<br>(M) |FLOPs<br>(G)| weights | | ------ |:---: | :---: |:---: |:---: | :---: | :----: | |[YOLOX-s](./exps/default/yolox_s.py) |640 |39.6 |9.8 |9.0 | 26.8 | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EW62gmO2vnNNs5npxjzunVwB9p307qqygaCkXdTO88BLUg?e=NMTQYw)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_s.pth) | |[YOLOX-m](./exps/default/yolox_m.py) |640 |46.4 |12.3 |25.3 |73.8| [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/ERMTP7VFqrVBrXKMU7Vl4TcBQs0SUeCT7kvc-JdIbej4tQ?e=1MDo9y)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_m.pth) | |[YOLOX-l](./exps/default/yolox_l.py) |640 |50.0 |14.5 |54.2| 155.6 | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EWA8w_IEOzBKvuueBqfaZh0BeoG5sVzR-XYbOJO4YlOkRw?e=wHWOBE)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_l.pth) | |[YOLOX-x](./exps/default/yolox_x.py) |640 |**51.2** | 17.3 |99.1 |281.9 | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EdgVPHBziOVBtGAXHfeHI5kBza0q9yyueMGdT0wXZfI1rQ?e=tABO5u)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_x.pth) | |[YOLOX-Darknet53](./exps/default/yolov3.py) |640 | 47.4 | 11.1 |63.7 | 185.3 | [onedrive](https://megvii-my.sharepoint.cn/:u:/g/personal/gezheng_megvii_com/EZ-MV1r_fMFPkPrNjvbJEMoBLOLAnXH-XKEB77w8LhXL6Q?e=mf6wOc)/[github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_darknet53.pth) | </details> #### Light Models. |Model |size |mAP<sup>val<br>0.5:0.95 | Params<br>(M) |FLOPs<br>(G)| weights | | ------ |:---: | :---: |:---: |:---: | :---: | |[YOLOX-Nano](./exps/default/yolox_nano.py) |416 |25.8 | 0.91 |1.08 | [github](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_nano.pth) | |[YOLOX-Tiny](./exps/default/yolox_tiny.py) |416 |32.8 | 5.06 |6.45 | [github](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_tiny.pth) | <details> <summary>Legacy models</summary> |Model |size |mAP<sup>val<br>0.5:0.95 | Params<br>(M) |FLOPs<br>(G)| weights | | ------ |:---: | :---: |:---: |:---: | :---: | |[YOLOX-Nano](./exps/default/yolox_nano.py) |416 |25.3 | 0.91 |1.08 | [github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_nano.pth) | |[YOLOX-Tiny](./exps/default/yolox_tiny.py) |416 |32.8 | 5.06 |6.45 | [github](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_tiny_32dot8.pth) | </details> ## Quick Start <details> <summary>Installation</summary> Step1. Install YOLOX from source. ```shell git clone git@github.com:Megvii-BaseDetection/YOLOX.git cd YOLOX pip3 install -v -e . # or python3 setup.py develop ``` </details> <details> <summary>Demo</summary> Step1. Download a pretrained model from the benchmark table. Step2. Use either -n or -f to specify your detector's config. For example: ```shell python tools/demo.py image -n yolox-s -c /path/to/your/yolox_s.pth --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result --device [cpu/gpu] ``` or ```shell python tools/demo.py image -f exps/default/yolox_s.py -c /path/to/your/yolox_s.pth --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result --device [cpu/gpu] ``` Demo for video: ```shell python tools/demo.py video -n yolox-s -c /path/to/your/yolox_s.pth --path /path/to/your/video --conf 0.25 --nms 0.45 --tsize 640 --save_result --device [cpu/gpu] ``` </details> <details> <summary>Reproduce our results on COCO</summary> Step1. Prepare COCO dataset ```shell cd <YOLOX_HOME> ln -s /path/to/your/COCO ./datasets/COCO ``` Step2. Reproduce our results on COCO by specifying -n: ```shell python -m yolox.tools.train -n yolox-s -d 8 -b 64 --fp16 -o [--cache] yolox-m yolox-l yolox-x ``` * -d: number of gpu devices * -b: total batch size, the recommended number for -b is num-gpu * 8 * --fp16: mixed precision training * --cache: caching imgs into RAM to accelarate training, which need large system RAM. When using -f, the above commands are equivalent to: ```shell python -m yolox.tools.train -f exps/default/yolox_s.py -d 8 -b 64 --fp16 -o [--cache] exps/default/yolox_m.py exps/default/yolox_l.py exps/default/yolox_x.py ``` **Multi Machine Training** We also support multi-nodes training. Just add the following args: * --num\_machines: num of your total training nodes * --machine\_rank: specify the rank of each node Suppose you want to train YOLOX on 2 machines, and your master machines's IP is 123.123.123.123, use port 12312 and TCP. On master machine, run ```shell python tools/train.py -n yolox-s -b 128 --dist-url tcp://123.123.123.123:12312 --num_machines 2 --machine_rank 0 ``` On the second machine, run ```shell python tools/train.py -n yolox-s -b 128 --dist-url tcp://123.12